A Distance Model for Rhythms
| Type of publication: | Conference paper |
| Citation: | paiement:ICML:2008 |
| Booktitle: | 25th International Conference on Machine Learning (ICML) |
| Year: | 2008 |
| Note: | IDIAP-RR 08-33 |
| Crossref: | paiement:rr08-33: |
| Abstract: | Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of distances between subsequences. A specific implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two different music databases. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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